An Optimized Deep Learning Based Attendance Management in Education with RFID

In education, attendance management is an essential criterion and traditional manual model are generally take more time and error-prone. This study proposes a deep learning (DL) based attendance management system utilizing Radio Frequency Identification (RFID) for automation, accuracy, and efficiency. A new method for monitoring presence of the students in class is introduced in this research, which makes use of RFID and the DL model. The system uses an Atmega328P microcontroller integrated with the MFRC522 RFID reader for real-time attendance data collection. RFID tags allocated to students are scanned during arrival. Then, the data is processed using a DL model deep belief network (DBN) for ensuring authentication and preventing duplication. The hardware setup focuses on the physical connection and synchronization among the microcontroller and the reader and ensures robust and reliable data capture. The analysis shows the effectiveness of the system and enhances the attendance tracking efficiency in schools. This research provides an establishment for the development of scalable and secure attendance solutions for modern education systems.